{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "from statsmodels.tsa import stattools\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Start by setting the current directory and henceforth working relative to it\n",
    "os.chdir('D:/Practical Time Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#read the data from into a pandas.DataFrame\n",
    "air_miles = pd.read_csv('datasets/us-airlines-monthly-aircraft-miles-flown.csv')\n",
    "air_miles.index = air_miles.Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the DataFrame: (97, 2)\n"
     ]
    }
   ],
   "source": [
    "#Let's find out the shape of the DataFrame\n",
    "print('Shape of the DataFrame:', air_miles.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>U.S. airlines: monthly aircraft miles flown (Millions) 1963 -1970</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01</th>\n",
       "      <td>1963-01</td>\n",
       "      <td>6827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02</th>\n",
       "      <td>1963-02</td>\n",
       "      <td>6178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03</th>\n",
       "      <td>1963-03</td>\n",
       "      <td>7084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04</th>\n",
       "      <td>1963-04</td>\n",
       "      <td>8162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05</th>\n",
       "      <td>1963-05</td>\n",
       "      <td>8462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-06</th>\n",
       "      <td>1963-06</td>\n",
       "      <td>9644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-07</th>\n",
       "      <td>1963-07</td>\n",
       "      <td>10466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-08</th>\n",
       "      <td>1963-08</td>\n",
       "      <td>10748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-09</th>\n",
       "      <td>1963-09</td>\n",
       "      <td>9963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-10</th>\n",
       "      <td>1963-10</td>\n",
       "      <td>8194.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Month  \\\n",
       "Month              \n",
       "1963-01  1963-01   \n",
       "1963-02  1963-02   \n",
       "1963-03  1963-03   \n",
       "1963-04  1963-04   \n",
       "1963-05  1963-05   \n",
       "1963-06  1963-06   \n",
       "1963-07  1963-07   \n",
       "1963-08  1963-08   \n",
       "1963-09  1963-09   \n",
       "1963-10  1963-10   \n",
       "\n",
       "         U.S. airlines: monthly aircraft miles flown (Millions) 1963 -1970  \n",
       "Month                                                                       \n",
       "1963-01                                             6827.0                  \n",
       "1963-02                                             6178.0                  \n",
       "1963-03                                             7084.0                  \n",
       "1963-04                                             8162.0                  \n",
       "1963-05                                             8462.0                  \n",
       "1963-06                                             9644.0                  \n",
       "1963-07                                            10466.0                  \n",
       "1963-08                                            10748.0                  \n",
       "1963-09                                             9963.0                  \n",
       "1963-10                                             8194.0                  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see first 10 rows of it\n",
    "air_miles.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's rename the 2nd column\n",
    "air_miles.rename(columns={'U.S. airlines: monthly aircraft miles flown (Millions) 1963 -1970':\\\n",
    "                          'Air miles flown'\n",
    "                         },\n",
    "                inplace=True\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values found: 1\n"
     ]
    }
   ],
   "source": [
    "#Check for missing values and remove the row\n",
    "missing = pd.isnull(air_miles['Air miles flown'])\n",
    "print('Number of missing values found:', missing.sum())\n",
    "air_miles = air_miles.loc[~missing, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BNBqNZl2SzpdJRIKEA35Xs0G0DKLRaDQukjI966qUOhtEo9Fo1itpU7MO62wQjUajWZ+U\nK1VypYqpWesKRo1Go1mXqCkxKsCos0E0Gs2m5+xyjlve/S1Onc+u9aFYqMEDiUiAcFDLIBqNRsNj\nZ5IcnctweDa11odioZo4DdhkEGOWeO9oY63RaDYkC5kCANmie1JDr6gpMQkzwAi4lmutjbVGo9mQ\nzKeLAOTWkbFOmZ610qzBvQEE2lhrNJoNyYIy1i4G8TphKVtsu0551qo3CLg32ksba41GsyGZTxsy\nSD8963PLea5/x79z16G5luvTlmcdtHnW7hyfNtYajWZDshaa9ZmlHOWq5KFTSy3Xt9KstQyi0Wg2\nNUoGyfdRBknmjNS8Y3PplutThTJCQCzoJxzQMohGo9FYAcZ+etbLyljPZ1quT+fLJEIBfD5BOKhl\nEI1Gc4HzvaPznFnKtV1frUrOmzJIPwOMybzyrDMt86fThRKJiNEfT8sgGo3mgucNn/wR7/vPI23X\nL+VKVE1b2c8A43LWMNbpQpm5VKFpveplDdRkEG2sNRrNhYiUkqVciekVPGuVCQL99ayVDAJwdK5Z\nCknly82etUvHp421RqNZV2SLFaQ00uTaoYy1EJAtlvt1aCznSgT9AoBj881BxnShzEAkCEAkqGUQ\njUZzAaNylc+uYKxVJsi2wQg5F6exrEYyX2LPWJxI0MexNp71gEcyiJ4Uo9Fo1hXKWC/nSmSLZWKh\nZjO1YHrWO0ZiLOXaVxS6zXKuxHAsSMCfaJm+l87bNWudDaLRaC5gVGEJtJdCFjJFfAK2DUf6rFmX\nGYoG2TcRb5m+ly7YNWudZ63RaC5glGcN7Y31fLrAaDxEPBwgV+yjDJIrMRgNsn88zqnz2TqvuVqV\n9dkgWrPWaDQXMimbZ91Ot55PFxlPhIkF/eT6GGBM5koMRoLsm0hQlXByoTb4IFOsNXECCPm1DKLR\naC5g6jzrZBsZJF1gLBEiGvKTK1Vca/C/EpWqJFWoySBQn76XtrVHBfD5BEG/0J61RqO5MMmYRi/g\nE5xdbp1rvZApMhYPEw35qUr3pIaVUH1BhqJB9o4bxtqevmdv4qQIB/xas9ZoNBcmykPdPRZrH2BM\nFw3P2uwZ3Y9mTqrUfCgaZCASZHIgXJe+l2rwrMHICClWtAyi0WguQFL5MiG/j91j8Zaadb5UIV0o\nG5p1yDDW/WjmpKoXB6NG0cu+iXhd+p598IDCmHCuPWuNRnMBopohbR2KtPSsVfXieCJExPSs+5G+\nt2yTQQD2TSTq0vfsgwcU4aBfa9YajebCJFOokAgH2DYYYSFTbJI4VPXiWDxsySD9aObUZKzH4yxl\nS5zPGMfTWrP26WwQjUZzYZLKl4mHDc8aYDZZ391OTYgZS4Ss6sZ+eNbJnGGMB6PGa6qMECWFtNOs\ntWet0WguSNKFEgPhANuGogBNGSFq6MB4Ikw0ZJiwfmrWNc86AWAFGS3POqyzQTQazSZAlWwrz7ox\n19qSQRIhokHTs+6TsQ76hSW97BiJEvL7+NdHz5IrVkgXSsRCfvw+YW0TDvZRBhFCfEQIMSuEeLTF\nut8XQkghxLht2VuEEEeEEE8KIV5gW36dEOIRc917hRDCXB4WQnzWXH6vEGKPK+9Mo9FsSFQzJGWs\nGzNC5tMFYiE/sVCAaEgFGL2vYkzmSwxFg5imi4Dfx+8+7yK+9eQcL37fd/nx2VSdVw39l0E+Cryw\ncaEQYifwfOCkbdllwG3A5eY27xdC+M3VHwBeCxw0/6l93g4sSikPAO8B3unkjWg0mguDdKFCPBwg\nEQ4wEAk0ZYSo6kXASt3rR3+QZbPU3M5v3ryfO19zA3PpAt85Ml8XXARTBumXsZZS3g2cb7HqPcCb\nAHud563AZ6SUBSnlceAIcIMQYhswKKW8Rxp1oR8DXmzb5k7z8eeBW5TXrdFoNh/pQsnKVd42FGnS\nrFX1ItDX1D3VxKmRZ180wb/89k9w08FxnrF3tG6dm9kgjvpZCyFuBc5IKR9qsKtTwD22v0+by0rm\n48blaptTAFLKshBiGRgD5p0cm0aj2biUKlXypaolJ2wdijZ51vPpIlPDhkRS86y9l0GWcyVGYqGW\n67YNRfn47c9oWh4OrmFRjBAiBvwR8CeuHEF3r/06IcT9Qoj75+bm+v3yGo3GYzIN6W/bBiNNmvVC\numB51kG/j4BP9M2zHmrhWa9EX2WQFuwH9gIPCSFOADuAHwkhtgJngJ225+4wl50xHzcux76NECIA\nDAELrV5YSvlBKeX1UsrrJyYmHBy6RqNZz1hVgBHlWUeYSxcoVQyDV61KQwZJ1DzcaMjft9Q9lWPd\nKWtaFCOlfERKOSml3COl3IMhaTxNSnkO+DJwm5nhsRcjkHiflPIskBRC3Gjq0a8EvmTu8svAq8zH\nLwG+KfvR71Cj0aw7GtuMbhuKICXMpoxCmOVciUpVMp4IW9tEg37PGzlJKUnmyw48ayMbxA2T1knq\n3qeB7wMXCyFOCyFub/dcKeVjwOeAx4GvAW+QUqqz+HrgQxhBx6PAV83lHwbGhBBHgN8D3uzwvWg0\nmg1OY2GJlWttBhnt1YuKWB8860yxQqUquzfWQT9SQqnSu7Fe1aeXUr58lfV7Gv6+A7ijxfPuB65o\nsTwPvHS149BoNP1HSsnrP/kjbrthF8++yHvpMdUgg9SqGA3d2l69qIgE/Z4XxVgd9yLde9ZgTIsJ\nBXqrQdQVjBqNpi3nM0W++ug57j3WMozkOlab0QbP+uySYazt1YsKNS3GS5az9aXmnVIz1r0HGbWx\n1mg0bVEebT8CeFDLBombxnowEiAW8nN2OU+lKvnhU4sAVjYIGDJIvzxrJ9kg4I6xdpRnrdFoNgfT\nS4ZW3I9JLNCcDSKEYOtQhO8dnedFf/1tnpxJ8awDY4zFbZ510M9ipuTpcakpMa2KYlbCmnDuwvnT\nxlqj0bRFGet+edZqsnk8VDNN24eifOfIPHvH47zvFU/jRVduxV6MFw0FPL+YOPes3ZNBtLHWaDRt\nUTJIP4pOwPCs4w2d6/7wBRfzi9dN8TNXbSfob1Zuo0Gf5xeTZM6hZ61lEI1G0w+mTWPdNxkkX25q\nhnT1zmGu3jncdptYKODKxeSOf3mcLYMR/ttN+5rWLedKCFELfHaK5VlrGUSj0XhJv2WQdLFsBRc7\nJRJ0JxvkXx85x7lknhv3jXHF1FDduqTZcc/n667HnKVZ62wQjUbjJWdNY92P5v5geNbdeq+xkJ9i\nuUql2lvhSaFsFL68+QsPU67UG1cnpebgrgyijbVGo2lJuVJlxizz7mc2SKMMshpRl9qk5ooV9o3H\nefRMkv/z3RN165YdNHGC+qKYXtHGWqPRtGQ2VaBSlfhEH2UQc0pMN6hpMdke2qRKKcmVKrzoym08\n99ItvPsbT3JyIWutd9IXBGyetQttUrWx1mg0LVFN/3eOxvqbDdKtsTY963wP02KKlSpVaRj+t7/4\ncgI+H3/y5dokQ8eetalZFyvaWGs0Go+YNku8908k+mqsu9WsLc+6hzmMedPzjQT9bBuK8ps37+db\nT85xZDYFtB7p1QluZoNoY63RaFqiMkH2T8RdCeCthpTSmWZtTYtxbhCVJq+89F96+k6CfsGn7zsF\nOPesQ7o3iEZzYfHDp87z4KmltT6MOs4u50mEA0wOGM2UvPau8yXjgpAId2cUrQBjD8ZabRsNGSZx\nPBHm+Zdt5Z9+dJrlXIliudp1QQxAyK+NtUZzQfG2Lz/Gu772xFofRh3TSzm2D0eIuOC5dkKqYFQJ\ndutZW3MYe7iY5M1sjYgZEAR4xTN2sZQt8bkfGN61E2MdMMeO6WwQjeYC4cxijkyfMi46ZXo5x7ah\nKDEXPNdOyBSM/SfC/lWeWY/yrHvJWFHvTV2YAJ65b4zdYzE+9J1jQPd9QRThgDtDc7Wx1mjWmFyx\nwmK21JcJ3d1wdinP9uFITRP2WAapTYnpUgZx4fhyDZo1gM8nuO3pu5hJGrnmjo110J2hudpYazRr\nzLSZItevjItOyJcqLGSKbB+K2jxXby8mlgziMHXPjQBjJFjv1b/0+h0E/UaJeU+etZZBNJqNz3Sf\nS7o7QXXb2zYc7btnPdC1Zm08vyfN2pQpog3GWgUawRiE4AQ1NLdXdCMnjWaNUSOr1pWxNi8g24cj\ntaITr411w2TzTlG5zK5kgwSb9fI3/tQBfD7BjpGYo32HA35XNGttrDWaNeaM6mxXqiClrGusv1ao\nY9o+FLWq7zyfIN4w0qtTfD5BtMfOe2rbSKhZbLh02yB/8/JrHe87HNQyiEZzQaDKuqV0Jx/XDZQM\nsnUo4oom3Alqsnm3MgiYQ3M90KzdwC0ZRBtrjcZDppdyfOrek6s8J289Xi9SyNnlHOOJEJGg39Ks\nPZdB8mUCPmHJGt0QDfp78vwbKxjdJBzQ2SAazbrniw+c4Y+++AjnlvNtn6OyQWD9ZIScWcqzbSgK\nuJPH3Amq1NyJDBQN+Xu6mORKFQI+0XJsWK/obBCNZgOgpmIfmU23XC+lZHopx+RAGOhfK9LVOLuU\nY9uQUWbuVr/o1TDmLzoLoxmedW+NnLyQQMDUrHVRjEazvlHpaIfN7m2NLGZL5EtV9k8kgP41+V8J\ndQHZPmx41j5TmuhH6p4TvRpMzbpHz9ozY61lEI1m/ZMyjfWhmdaetdXZbjIOrA/POpkvkylW2D4c\nsZbFegzgdUK60P3gAUU02GOAsVixmji5jZZBNJoNgModPtLGs661ITU86/WgWavsFOVZQ+/GsBOc\ntEdVxNzwrANeedbts0He8oVHOt6PNtYajYekbZ61lM39oFWKnGWs10F/kIV0ETCq9xSRHo1hJzgZ\n6aVwIxskGvJKs25fFHP/ifMd70cba43GQ1Tu8HKuxLxpBO1ML+UI+X3sGDG82PXgWafMoKh9Msq6\nl0FcyAbxTrM2ZJBWF+uZZPssoUa0sdZoPCSVLzFhZnq0CjJOL+fZNhyxqvbWhWaday5O6bVCsBN6\n1ax7apFaqnqSYw2Gsa5KKDdM2skVKyTznd9JaWOt0XhIulDmabuGgdbpe9Nmipwbo6ncQqUb2pvt\nR3o0hqtRqUqyxUrPmnUr77UTCqUKkaBXAUZzwnmDbj2b6tyrBm2sNRrPkFKSzpfZP5FgIBLgcIuM\nkLNmily/Sro7IZlvbqgU61FmWA2nTZwUkZC/p3L9XKninWcdbD00V/XJ7hRtrDUajyiUq5SrkoFI\nkIOTiSYZpFypci6ZZ2o4StDvI+gX60azHggH8PtqlYReyyC9Gutep9nkih4GGNsMze1GrwZtrDUa\nz1A51olIgIOTA00yyEyqQFVilXV7LTV0SjLXXJwSDXl7bFnTWMd6CDCC8wBtrlSx5Aq3aSeDaGOt\n0awTVFbFQDjAwS0J5tNFzmdqGSH2ntFgeK/roYIxlS81DYeNBgPkPTTW7Zr/d0qkx/4lhVK1D551\n/bHNpgpdNa3SxlqzaShXqtbtdj+w39ofmDTyqO3etdUz2iw+iXnsvXZKMl9q4Vl7W26uDJmTjntQ\nmxbj5GJXrlQpVjzMBjE162ILz3rLYKTVJi3RxlqzafiHbx/n+X95V99eL22XQbYMAPXpe9boLLNh\nUqQP6XGdkMqX63KswfB4y1XZZHDcQkkETo11L50B8+XevPrVWEkG2TIYbrVJS7Sx1mwaHj69xPRy\nnkrVWXpXt9ib6W8fihAP+esyQqaXcgxEAgyYhrEfhSed0Nqz7n3O4Uooz9ppYUovmnVt8IA35lDl\n0C9nS3XLZ5MFJrVnrdE0c3w+A/SvSlAFGAfCQYQQHJhM1Mkg00tGJoii185xbpHKl1to1t4OIFCa\nddihwaylPnYvc6kLpFcVjHvGjNmNJxYydctnknm2DGhjrdlEVKqST9zz1IqdzaSUPLWQBWqZB16T\nNgOMqtDj4JaBOhnE3oYUjCDeWmvWUkpSLVqVxkLeDiCoadbODGbMBc/aqwDjcCzESCzI0bmasU4X\njM6GWgbRbCp+dHKRP/7nR/n6YzNtnzObKlg/5H4ZxMbc4YOTCWaSBZ48l+Jrj57j1PmspVdD7/0t\n3CBbrFCpyibNOuJx0Y5qdORUioj2cDGxhuV6lLoHsG8iwbG52l2VStvrJsCop5trNjxLphb4+Nkk\nP3v19pYAWrAKAAAgAElEQVTPOTFf82r6ZaxThTLhgI+QGTQ7uMXICHnBX90NQMAneMa+Mev5sR6n\nnbiBKjUfaAww9pjHvBrqIuXUs+6lXN9KG/TIswbYOx7n7kNz1t/KWE924VlrY63Z8Kh85h+fTbZ9\njpJAAHKlfskg9XLCf9k/zutv3s/WoQhXTg1x6bbBOp201wndbqB09sFoaxnEM8/apWyQVseXL1VY\nzpXaerGWZ+2RZg2wbyLO53942qgOjQSZNUvNtWet2VQoA7OSsT6+sAaedUN/5kjQz5teeEnb56+H\nAGMy18az9ngOY6/GOuj3EQ36rTsDO39/1zE+fs8JfvDW57YcxqsMvFepewD7xo27qhPzWa7cMeRI\nBln1zAghPiKEmBVCPGpb9r+FEE8IIR4WQnxRCDFsW/cWIcQRIcSTQogX2JZfJ4R4xFz3XmGeNSFE\nWAjxWXP5vUKIPR0fvUZDzbOeSRZYSLdujvPUGhjrbiefRIN+ShVJqeJNLnMnWJ51w3HXKgS9uSvJ\nm9PFAz1MFx+NhzifaTbWJ89nmU8X2xZE1dIGvQvh7ZswxrYdmzd065lkgXjI31UvlE6O7qPACxuW\nfQO4Qkp5FXAIeAuAEOIy4DbgcnOb9wsh1OXqA8BrgYPmP7XP24FFKeUB4D3AOzs+eo2GmoEB+PHZ\n1uOzjs9nrRSqfkkN3U4+6SWjwS3aadbq2LwKgBbKVcdetWIkHmQx2zzgQS1rNfwBbJ61h5r17rEY\nQsAxMyNkJtVd9SJ0YKyllHcD5xuWfV1KqX4h9wA7zMe3Ap+RUhaklMeBI8ANQohtwKCU8h5pNJz9\nGPBi2zZ3mo8/D9wiWt2raDRtSObLllf0+NnlpvVG2l6Gy7YPAv0NMDYavZVQ3quXPThWI9lGs/a6\nhWuhXCHcowwxEgvV9V5RqGXzbe661MXRSxkkHPCzYyTKMTPQPZvMdxVcBHdS914DfNV8PAWcsq07\nbS6bMh83Lq/bxrwALANjaDQdksqX2DYUZetgpKVnPZcqkC1WuHSrMtb9CTCqVqOd4nUucycozbqp\n3Fwdm1eedalKpEfPejQeaulZW8Y61dpY5620Qe+MNRi69XGbDOK6Z70SQoi3AmXgk73sp4vXe50Q\n4n4hxP1zc3Orb6DZFKgijsu2D7YMMp4wM0Eu3WYY677JIA40a1hbGSSVLxPy+5okiXDAhxDeef35\nctUzz3qxQ8+6VxlmNfaOxzk+l0FK2XUTJ+jBWAshXg38DPDLsjZL5wyw0/a0HeayM9SkEvvyum2E\nEAFgCFho9ZpSyg9KKa+XUl4/MTHh9NA1Fxgps5fFpduMntGNlYwqx/rglgQBn/DMO7SjpsR0o1n3\nUtjhFqovSKMSKYTwdABBoVTp2ViOxkOk8uW6AG2xXLV6tMy10azz5pQYr9XX/RNxMsUKh2bSFMpV\nJgf6IIMIIV4IvAn4OSll1rbqy8BtZobHXoxA4n1SyrNAUghxo6lHvxL4km2bV5mPXwJ8UzodpKbZ\nlCTNLnGXbhukXJVN47NOLGQI+ARTw9G+5TLbp8R0itf9NzqhVV8QRa9DaVei4IpnbRy3XQpZsj2e\nayOD5IrezV+0s9dM37vnmOGLuu5ZCyE+DXwfuFgIcVoIcTvwt8AA8A0hxINCiL8DkFI+BnwOeBz4\nGvAGKaX6dF8PfAgj6HiUms79YWBMCHEE+D3gzV29A82mR3nWl5kyx+MNUsiJhQw7R2ME/D6zZ7T3\nmnWyoS9IJ6iezGutWTf2BVF4mQeed8GzHomHgFpFK8B5m7FuJ4Moz9prVPre9486M9arfpOklC9v\nsfjDKzz/DuCOFsvvB65osTwPvHS149Bo2mFo1kF2j8WJBv1NuvUJW9peLNSfZklpq+NeNzKIYazW\nVrMuNQUXFV5OsimUq20vEp0yGjOMtV23Vo9DAd+KmnXEw7Q9xdbBCJGgj3uPK2Pd/2wQjWbNKFeq\nZIsVBiLGgNdLtg3UGWuVtrd7zPBqosH+yCBOBsBaPaPXsD9IskXHPYWXcxgL5WrP2RjKs15sYaz3\nTyTW3LP2+QR7xxMsmp7/ZBftUUEba80GJ201+De8wUu3DfL4dBIV9phLF8gUKzbPuj+js+xTYjrF\n61zmTljNs/Y0z9qFACPUSx/KcF+0JcF8ql2AsfcLRafsGzechsFIoOsiHG2sNRsaq8G/aRQv3TZI\nMl9m2hyZpRo47TF/JNGQvy/ZIPYpMZ0S8ziXuRNaTTZXeNnCtVCq9jxdfFgFGOs8a8OLPTiZIFeq\nkGlRcp7rk2cNNd26W70atLHWbHBUIE/1srCCjNOGFKKmw+wxZRBjdJb3MoN9SkyneJ3LvBqlSpVc\nqdI2G8TLuxKjgrE3cxQOGL027P1BFrNFBiIBtg4ZQx5aZYQY2SDr31jrrnuaDU3NszYMzCVbBwj4\nBP/7354AjBzrgE+wY0RNEO9XgLH7bBCVy7xW2SCNdymNuDHQN5UvkSlU2DpUb6yMCsbeDWZjf5Dz\nmSJj8RDjCUMimU8XrLssRb7Un9Q9qKXvdVtqDtqz1mxwGg1MPBzgb19xLYVyldd+7H4+ePcxdoxE\nrW5u/cqzdhJgBDwtPFmNVL51qbnCjWyQd3/9EL/64Xubludd8KyhuYrxfKbISDzEhFmA0irI2K8A\nI2jPWrOJSbXoEvfCK7bx3Eu38MUHzvCBbx3lxv2N01j6o1nbp8R0yloOIEjmVvas3ZBBZlN5Zhuk\niEpVUqpIV8q9R2KhukKY85ki24YiTCQMY92qijFXqnjacc/OYCTIX992DdfvGe16W22sNRuadrfu\nAb+Pl16/k5dev7Nuecws7KhWJT6fd+XFrYbOdsK68KxXqGDMlSpIKR2XZmeLlaaLUbHsXiOl0XjI\n6hkNhmZ92fZBRuMhhGjdzKmfAUaAW6+ZWv1JLdAyiGZDU/OsOzOMKpc5v8IkdDfoti+Iol+pha1I\nrnIuIyE/UtamujghW6hQrFQp2/p31Cabu+NZL5oBRikl5zNFRuMhAn4fI7FQkwwipSRf6r3UvR9o\nY61xxMmFLA+cXKz70a0FqbwhN3Sa9tWvNqTddtxTuBHEc4rVy7qNZh1zIQ88a86/tKcnqhalvabu\nAYzGg6QLZQrlCrlShUK5auVfjyeajbW68PTTs3aKlkE0XbOQLvALH/ge8+kCg5EAzzowzq3XTPHC\nK7b2/ViS5gDSTullCnY39OJZt5to4jXtelkr7BPORxy+hrpI5ooV63XcHKtl7w+iuu+pMvTxRLgp\nda82f3H9+63r/wg16wopJW/+wiMkcyXefuvlvPCKrfzgxCJv/NSPmlqT9gOj4976a/Df7UVEsZZD\nc1OrVF3W5jD24FkXKk37qA3LdcGztvUHUVkhI/GasW68EOatC4X2rDUXGP94/2m+8fgMb33Rpfzq\nM/cA8KUHz/A7n3mQ4/MZLjGnsfSLbgN5NWPtbWFMulDuqomTIhoMrF02SL5EImz0WGmF6grYS/qe\nOu/286/254ZmPRyr9QcpKs86blw0DWPdxrPuUzZIL2jPWtMxJxey/NlXHuOZ+8a4/Sf2Wssv2jIA\n0NRHuh+kupVBgqpZ0vrUrKMh35p61ivdpbgxyUZ51C09axekCHt/EFUcM2Ia8ImBMNlipe5Cod7L\nRvCstbHWdMwf/OND+HyCd7/s6rq0t73jcXwCDs+uhbF26ll7ZxCdTIlRGBWWa9N1z+hl3f7Cp1q4\nOj13RXMgQ+M+Ci7OQByJqwEEJavsfCxu5FhbVYy2hk55baw1FxqzyTz3nTjP628+wPbhaN26SNDP\n7rE4R2abh9V6jRo80Cn9aJbkZEqMIhL0ky9VqVb7PyxptQtfr3cl9u1yHskgIzYZZDFTxO8T1nsa\nH1CFMTUpRGWibIRsEG2sNR3xmNkY6brdrfMADkwm1kgGKTvMBvHOe3UyJUahLiZe5YG//1tH+N7R\n+ZbrkvlS24IYqJ07p5p1xnbOvQowBv0+BiIBzmeKLGSKjMSC1l2gVcVoywipZYNoY625QHj0zDIA\nl21vHUA8OJng+HzGqkbrB/bBA53Sj9FZTqbEKLzsaV0oV3j31w/x0e+eaLl+Nc+6VwnJvl29sXYv\ndQ8M3Xoxa3jWytMGI8AI9f1BlGatJJ71zPo/Qs264NHpZfaNx9vqsAe3JChXjaks/aJx8EAn9EOz\ndtrECbydcH58PkOlKnnEvPA2klxh8ADUdF2nAcZ6GcQbzxpqzZzOZ4tW2h7AmK3znqImwWjPWnOB\n8Nh0sq1XDXBw0swI6WOQcbWWnq1QPaO9zAZxMiVG4eWEcyVTnV3OM5vK162TUnagWXsjg7ipWYMx\n5Vx51qM2zzro9zEcC7Y01jp1T9MTD5xc5F1fe4LKGgSb7Cxli5xezHHF1FDb5+yfSCBEf9P3kqu0\n9GyFEMLzzntJBxcRhZeev/1C+miDd50tVqhU5YqadSjgI+ATjrNV7BdIVXYONc/arYyMkbjRH2Qx\nW2Q0EapbN5EI12WDWDKI1qw1vfC5+0/x/m8d5V1mI/21QgUXr9je3lhHQ352jsQ43MeMkJTVy6LL\nntGhALmSdwFGS57pYkqMwo1c5nYcmU2xdTCCEPDI6foJ8J3epRhzGJ3FJeyeda5F6l637WTbMRoL\nsZApsJgt1XnW0FwYo96LTt3T9MTRuQxCwN/fdYwvPXhmzY5DeWGXryCDgBFkPNJHGUT1sug2Rc7r\nznZOpsQovOxdcmgmzVU7htg/keCRM0t16zq9S4mE/I4vdPZzninYZJByhaBftK2c7JaReIh8qUql\nKus0azDS9+pkkHKFkN/n2mt7iTbW65hjc2l+/popbtg7yv/3Tw833br2i8emk0wNR5u++I0c2JLg\n2Fymb534nGjW0Adj7UKA0W3PuliucmI+w8EtCa6aGuLh0/XfpU5bzcbaDEc4vZjllR+5j+VcqcVW\nBmq7kViwzuC7NdJLMWr7nqpSc8V4ItSUutevkV69sjGOchOynCsxny5y0dYB3v/LT2M0FuLXP/5D\n60fVTx6dXl7VqwYjyFisVHnqfLYPR9V9L2uF19NYVNtWJ7f1sWDvqYX3Hlvgpnd9s27K91MLGcpV\nycHJAa7cMcRsqsBMshZkVFNiVtKsof1whPtPLHL3oTl+fDbZYisDJYOMJ8JNqXtulJor7Ol6Iy1k\nkIxtAEK+j1NiekUb63XKsTlDTtg3Hmc8EeZdL7maM0s5vntkoa/HkS6UOT6fWTG4qDg4aQwD7VeQ\nsXFYbqcYnrV3mnWq4GxKDEDEzPftxbN+/GySU+dz3H14zlp2yPxMDkwmuGqH8VnavevGKfFtj69N\ncFb14bDPP2wkV6wghCFTNBbFuJk6Z/esVam5YqIh19oYlquNtaYHjs0Z+cr7TQP49L0jhPw+Hjy1\ntNJmrvPjs0mkhCumVvesD5jH2q+yc8dzDoPeTjh32hcEakU7vVRYqovYtw/XKhUPz6YQwviMLts2\nhE9Ql2+92uABxVA0aMUK7CxljWULLQbSKrLFCrGgn3jDnU2+VHEtbQ8MmcV63CCD7BqLAcYFDfo/\n0qsXtLFuQMq1TZNTHJtPE/AJdo0aX65wwM+l2wd54ORiX4/jMfMHvVImiCIeDjA1HO1brnW3HfcU\nMY97Rs+lCqvq++2ImEbLacYF1DTzbx+es77Ph2fT7BqNEQn6iYb8HJwc4JHTtQv/gyeXCPrFqjLI\naDzE+Wyz96yG1C6s4Flni2WioUBTs6pC2d2xWiN1mnX95/C0XSMMhAP85xOzAORKVe1Zb0TOZ4o8\n/Y5/518ePrvWh8KxuQy7RmME/bWP6Nqdwzx8ermvo7QenU4ynggzORjp6PkHt/SvR0i3gwcUXgcY\nD8+mLUmoWwJ+HyG/ry4PuVuUZz2TLFjZOUdm6o/pyh1DPHJmGSklD59e4gsPnObXnrV3VcM1HAuy\nlGn2rBdNz3olGSRbrBAP+5tiBoYM4p4pGjYvOOGAr8lrDgV83HTRON98YtaYv6gDjBuTT993kvl0\nkR+cOL/Wh8KxuQz7JuJ1y67dNUyuVOHJmf7lMj96ZrkjCURxcDLB0bl0Xwp5HE8Q9zDAuJgpMp8u\nWBWdToiG/OR7OL5UvmT1Jbn78DylSpVj82kO2I7pqh1DzKeLTC/n+bOvPM5YPMRv/dSBVfc9GguR\nKpSbesAozXphhZFkmYIhOcRC/oYZjO7KIAG/j6Fo0Jxo3pyS91OXbGE2VeCx6ST5spZBNhzlSpVP\n3vMUAEfn+t89zk6lKjm+kGHfRL13du1Oo+Ndv3TrfKnC4dl0RxKI4uDkAIVylVN9yAjpRQbJFsue\nSF5HzO/OgS3OPGswMi568fzThTJ7J+Lsm4jz7cNzPLWQpVSRdZ61Chjf8S+P88OnFnnTCy7p6FwO\nqxmHuXqjrFL2FjLtNetcqUw8HCAa8lvjvcDwrN2WIkbjoSYJRHHzxRMIAf/x41lyRZ0NsuH49x/P\nML2cZzwRtoJ7a8WZxRzFcpV94/We9c7RKGPxEA+c7I+xPjSTolKVHaXtKXaaGvv0Us6rw7JI5csM\nRp01+K/KWpmzmxwy73rU9Bwn9KqpqzuOmw6Mc++x8zw2vdx0TJdtG8TvE/zrI+e4cmqIl1y3o6N9\nj1r9ouulkE6yQbLFCrGQn1gwQLFSteS8gsueNcDusRi7zWBiI+OJMFfvGOabT8yQK1VczfH2Em2s\nTe783lNMDUd5xTN2Mb2c86SRTqccnTe8s/0NuqcQgmt3DfctyPjEOcPwXLqtc2M90aLBu1cYt/s9\nlHR70X9jJk085Gf7UGcafysiwd5kGpWNctPBCXKlCp+57xQA+ydrF/9I0G8Z7z/9ucvqJv+shMq0\nWGwIMiodeyUZJGuTQaA2AKLocoAR4L0vv5Z3veTqtutvuWSSh04vM58uENGe9cbh0EyK7x9b4Fdu\n3M3ByQRSGu0k1wrl2Td61gDX7Bzm6FyG5az3xTGHzqWIBH2Wt9wJlrFO9cNYO9OsvZwWc2Q2zYHJ\nREuttFN696xLJMJBbtw/RsAn+P6xBXaMRK20QMXtP7GX33/eRVy3e7TjfatMC3vBTalSJVUo4/cJ\nFrPFtlNusjYZBKgrTIm47FkPRoIrpk/+1KWT5mtXtWa9kbjzeycIBXz80tN3WkG9tZRCjs2lrQBJ\nI9fuMnTrh057L4U8OZPi4ORAV30TBiMBQgGf58a6NnjAgWft4bSYQzMpDvYggYBxfL1o1qooJxEO\n8DRzsk+r7JSXXLeD37rlYFf7VhWB9vQ9pVfvHo1RlbDUpuQ8WzD04Xi4vrOgkbrXX1N02bZBtpoZ\nTtpYbxCWcyW+8KMz3Hr1dkbjIfaOK2O9dkFGlQnSyju7ascQQtAX3frJc6mutVchBBOJsOfG2mlf\nEPBuWsxytsRsquA4bU8RDfody3BSStK2CsqbDowD9HwBUQybMsiS7c5O5VirgHi7wphssUI85Ldm\nOapca7crGDtBCMFzLjG8a526t0H47pF5cqUKt92wEzB+yNuHIhxbSxlkPs2+8dY/+IFIkIOTCR44\n5a1uvZgpMpsqcMnW7n/kEwNhzzXr3oy1Nz2jj8wZGv/BHjJBoDfPOlOsIGXtvNx8sWGQLt3mjrGO\nmJqzPZCocqyVJt6qMKZalUa1YChgnX+7DOJ2gLETbrGMtfasNwRKm75kay2Itm8isWaedSpfYiZZ\naMqxtnPtzhEePLXkabWlyuW+yKmx9tizTuadtUcF79qQqmKgXnKsoTfN2ppSYwZer9wxxOd+/Zn8\n7FXbezomOyOxUF2AUXnZ+03PulVGiHo/sZC/7mJZrhiT4NfCYD7rwDjPvmii7RDo9YY21vMZJgfC\nxG3BiH0TcY7NZdak9FxdPPavZKx3DbOULXFiwbtcZpWCdrGD2+fJPhhrp4MHwDvP+tBMmmjQz9Rw\ntKf99JINkmrRS/uGvaME/C723ogH6wKMynDvX0EGUec6HvLXzZksVtT8xf6bomjIz52vucGKA613\ntLGez7CnIeti33icVKHcl/SzRqwGThPtb6Wv2TUM4GkK3xPnUgxFg2wZDK/+5AYmBsIsZIqUPCyL\nT/XgWccaNFO3ODyb4sBkouM0uHYMhANkimVHVaCpgnN5qFMMz7pZs1YORisZRF18VG8QMM5/vrR2\nxnqjsenP0In5DHvHGoy1aSjXIiPk2Fwan6h1B2uFMuRPeelZn0tx8ZYBRyloKn1vpZzbXulFs/aq\nwf+R2XTPejXAcCyElLTsbrcaSgYZcNj1rxMaZZDFbImATzAUDTIUDbaUQVQv60YZpFA2PoONohuv\nJZvaWCfzJRYyRfY2SA61jJD+G+uj8xl2jsZWjI4H/T5G4yFmPZIapJQ8OZPioq3ODI/qGeylFOJ0\n8AB4I4Mk8yXOLud71quh1tazsfCkE5z2+O6G0XioTgZZypYYjgURQjAWD7W8SKtzHbPJILlixZq/\n2O/UvY3Ipj5DJ0x9eE+DZz01HCUc8K1JkPHU+Sy7x9rr1QpDF86v+jwnnF3Ok8qXuXhr55WLdmpV\njN4cHxijxgYiAYZj3bciVXm1bhpr1d2u17Q9wHpPiw4Kn9IF5/MfO2UkFiKZL1vl4kvZonXMY4lQ\ny/4gWcuzDhAL2j1rJYNoz3o1NrWxVsG8vQ2atc8n2DseX5P0vZlknq0d6MQTA2HPPOsnewgugvdV\njJWq5JtPzPKciycdDTr1+QSRoM/VopgjZiZILz1BFKrF53KuF8/aQ2Ntev6q+GUxW7TK0EfjoZYy\niN2ztreBVfnkGyXXeS3Z1GdIGetWDV/2TcT7XnJeqUrmUgW2dNA7enIgwmzSG2N46FxvxnrcYxnk\nRycXWcgUed5lWxzvw2iA755nfWjGKM2fGuktEwRqVYKNzZI6QRnreMhbzxpqJeeGDGIsG42HW8og\nOZuxhlqbWu1Zd86qxloI8REhxKwQ4lHbslEhxDeEEIfN/0ds694ihDgihHhSCPEC2/LrhBCPmOve\nK8zIlRAiLIT4rLn8XiHEHnffYntOzGeYGo62DG7sG09w8ny2qW+vlyykC1QlHTX6nxwMM58utO3D\n0AtPnkuxdTDCUMyZ7hkJ+hmMBDwz1t94fIagX3DzxROO9xHtsVlSI4dn0+yfSDjy9BuxjKFDzToe\n8rtyHO0YaZBplrIl625gPBFq2R8kY5NBwEjhswcYdTbI6nRyhj4KvLBh2ZuB/5BSHgT+w/wbIcRl\nwG3A5eY27xdCKEv4AeC1wEHzn9rn7cCilPIA8B7gnU7fTLccX8iyZ7x11sW+iTiVquRknyZ1A5as\nsWVgdRlkciBMuSpbjljqFSO42NvtvFdVjFJKvvH4DDfuG+spiObmtJiHTy/x4KklV/RqMCQMn6gv\n6e6UdMFZj+9uUDKIkjsWs0WrwdNoPNSyP4jlWYfrPeta6p72rFdjVWMtpbwbaBydcitwp/n4TuDF\ntuWfkVIWpJTHgSPADUKIbcCglPIeaVSafKxhG7WvzwO3CCf5Yg44MZ9pCi4qaul7/QsyziSNgFxH\nnvWA8Ry3pZBypcrh2TQX95iCNjkQ8cSzPjqX5vh8huf3IIEATdNKnFCpSv72m4f5hfd/j3jIz2t/\ncl9P+1P4zDS4xgb/nZAulD0NLkLNs17KFi0pY9imWUNzYYylWZt3sWoOYy11T3vWq+H0DG2RUqpB\nhecA9cuZAk7ZnnfaXDZlPm5cXreNlLIMLANjDo+rYxYzRZZzpabgosLqvtdH3XrGNLydFKJMms+Z\ndTkj5ClT+nGaCaLwKgD6jceNQafP7dFYG56d8wBjqVLlFf9wD3/x9UO86MptfPW//ySXdzFRZzUa\nC086xWnb2G6wd95TF5ThqLFMxSsaC2MyxTKhgM+qpIyG/GTsqXvas16Vni9npqfcl7psIcTrhBD3\nCyHun5ub62lfxxdaZ4IoBiNBc2pMfz1rIWpf+JWYHFDG2l2D2GtwUeFVf5BvPG5MNtk21Fsgr9cA\n45HZNPceP88fPP8i3vvyaxlaZSp4twzHglZlYDekzMEDXhIN+YkEfSxlS1YQdKTBs27MCMmZU2IU\nscYAo/asV8XpGZoxpQ3M/2fN5WeAnbbn7TCXnTEfNy6v20YIEQCGgIVWLyql/KCU8nop5fUTE86D\nSwDHzYKXxlJzO9uGIp6lx7ViNpVnLB6um2jeDiWDuG0Q1Z2EfaqIEyYGwmSLFTIF99Lj5lIFHji1\nxHMv7c2rht6H5qbN93X1zuGej6UVw7GQI83amEvprbEGY7zX+UzRuqBYedZtZJBMoWJJIFCbg2ml\n7mnPelWcGusvA68yH78K+JJt+W1mhsdejEDifaZkkhRC3Gjq0a9s2Ebt6yXAN2UfOiidWMjgE7Bz\npH1Z91A0aDVW7wczyULHvTiiIT8D4QCzSXdlkLPLOYZjwaapIt3iRRXjf/x4BinpKWVPEetxKG0v\nvUk6wfCsnQQYy45GnXWLcTEpWlKNCjqqQGOjDJIrlYnZPP5oMKA96y5Z9RcphPg0cDMwLoQ4DbwN\n+HPgc0KI24GngJcBSCkfE0J8DngcKANvkFKqX8TrMTJLosBXzX8AHwY+LoQ4ghHIvM2Vd7YKx+cz\n7BiJEVohZWgoFmR62fvBr4qZZL6jHGvFxKD7uvC55YI1QaMX7LMYV7p76YZvH55n+1DEld7MyrNz\nSspqReqNF9vYf6NT0nnvA4xQK35p1KyDfl/L/iDZFjJItlRL3Qu52BXwQmXVT1VK+fI2q25p8/w7\ngDtaLL8fuKLF8jzw0tWOw21OLDR322tkKBp01EzHKTPJAlft6DxINelBEO9cMsfWHoa9KryoYjyX\nzLO3zQSdbomGAj1PEAdnLVo7YTgatPKQOw2+VaqSTLHSFxlkOBZkeilnef/Dtpz8Vv1B1LBchUqd\nzJeqhAK+njsVbgY25eVMSsmJ+WzLgbR2hqLGrWg/+lqXKlUWMgVLi+6EyYGI69kgrnvWLhrrhXSB\nsdIHBs4AACAASURBVHj3LVtbEQv5KVWk4zauXjdMGjblhG4GIysd3esAI5iedbbIYqZINOivKyxr\n1R9EDctVREN+iuUquWJZF8R0yKY8S3PpAulCmT0rtCEFw7spV6XrTepbMZ8uICVdySCTA2FmkwXX\nLibFsnHB6OYY2jESC+H3CVeN9Xy6yFii+8ZNrei18166UMJv9hjxApVd0U36Xi+dCLtlOBZiOVfi\nfKZY51VD6/4g2WLF6rYHtXL4xWxJp+11yKY01ifmjarETmQQoC9Bxm5yrBWTg2EK5SrJvDsZF7Op\nPFIaWTC94vcZ7TLd8vzzpQrpQrmjtMZO6HW0l8pn9qp+y0nJebrgfXtUxWgsiJSGnNjY+bBVf5Bs\nwRiWq1DnfzFb1AUxHbIpz9KJNt32GlHG2klUvltU9WJ3nrVK33PHIFrH4IKxBuNi4pZnrbILVGpY\nr9Q8a2cXurTH+cxOvnteBz3tqKyP4/MZ6y5A0ao/SLZYrsswitmMtZZBOmNTnqWnzmcI+MSqs/L6\n6VnPWqXmXXjWqjDGpZLzc8vGftzQrMFI33OrP4jK2x1zybOujZZy5lkn82VPPVhlDBsLY753ZL5t\n7nq6D+1RFfZmTiNNnnVzf5BGGcQy1hktg3TKpjTW55YLTA6EVx0iqrrO9UsGMaSD7mQQcK+K8ayZ\npuiGDALuVjGq2+pxlzTrXi/EXheftNKsZ5N5XvGhe/nMD0613KYf8xdrx1f7HBq7Mzb2BymWjQnm\n9TKI0qyLOse6QzblWZpN5TtqljTUQxP4bplJ5plIhLtqbTmhmjm5KIOEAz7XSqcnBsLMp5vbZTph\n3vzhu6VZtyuL7hSj+MQ7oxgN+gkFfHXNnFR/9ZMLrfvVWJPN+1AUo4pggBYySH1/EPuwXIU9wKur\nFztjUxpro/hk9R99XwOMqc6rFxWDkQDhgM89GSRZYOtQxLWg2UQiTKUqHRV3NDJvetZuZYP00jMa\nvG+YJIRgOBpkyTaAQLXrPbPUulBrLWSQxsfQfCFUvazrPGtbqp/2rDtjU54lo6x7dc86EQ7g94m+\nadadePt2hBBMuljFeG4555peDTXP3w3deiFdIBr091wGr1DpZk6msUD/WpHaLybKWJ9ebG2sU/ky\nPkFdpaBXxEJ+q/q38U5MXVBVHCZredbNmjXowQOdsunOUr5UYTlX6shYCyGswhiv6dTbb8TNwphz\nybwr1YsKNwtjFjLu5ViDURY9EAk48qyllKZm7a3cMBwL1gXpLM+6jbFOF4wMlX60gxdCWPJHo2c9\nkQgzEgvy+NkkUD8sV2F/3GpSk6aZTWeslWQw2cE0FuhPM6dCucJitsSWLqoXFW6VnEspmVkurFtj\nPZ8uuJYJomg33HU1CuUqpYr0PEWusU3qUwuGsU4Vyi2/kymPM1QaUUbarl+DYciv2jHMQ6eWgZpn\n3SrPGrRn3Smb7iwpL7TTfOZ+GGvrAuLIsw4z54JmfT5TpFipuiqDqMyNeVdkkCLjLuVYK5w2S/K6\nL4iicQDByfNZy5s9vdg8bq5f7VHtxwc0FcWA0Tr28GyKTKFsCzC2k0G0Z90Jm85Y1yoFOzfWXjdz\nUheQbjVrtU3K9oNwyjlTX3TTWCfCAQI+4WjiSSMLmYJrmSAKp5611+1RFcOxEMtmb5pU3ijtfuZ+\nY4hSKylEySD9QgUSh1tkD129Y4iqhEfPLDcNywVDhlKd9nQFY2dsurNkzTnsQgZpHP7pNtYFxIEM\n4pbUoM6LmzKIEMLqe9wLUkoWXOwLohiJhVhsYawPz6SsnPNW9Kth0kgsSLFSJVusWHr1M/ePA62D\njKk+tUe1js+UP1qlel61wxjK8PDp5dr8xYbAp/K0tWfdGZvPWKfyhPy+puYz7RiOeS+D1ErNnckg\n0Huu9dll9401GAbHacaFYjlXolyVrmvWI7FgS6//Nz/5I97xLz9uu12qTylyVsZKtshJU6++ducw\n0aC/ZfpeutBfzfqWS7bwS9fvbFlcNjEQZmo4ykOnl2qTzRuMdcwy1pvODDmif5fhdcJsssDkYLjj\niLmSQapV6VnP3ZlkgaBfNEXVO8Gact6rZ72cxydqE17cYjgW7DnPet7l6kXFSDxErlQhZyuFllJy\nejG7ogGxik88N9aq5Lxkeda7xmJMjURbyiCpfKmvMshzLpnkOZdMtl1/9c4hHjq9xGXbjeHL8YZj\nszxrLYN0xKY7S91OYxmKBqnKWimvF8ym8kwORBxdDKyS8x7He51L5pnooAS/W5zOErRj9QVxqZe1\nQmmu9otJMl8mX6py6nxzAE9RCzB668WO2Iz1U2ZwcTASZGo4yumlVgHGsudBz264ascwp87nOLOY\nQ4hmD1p51jp1rzM2qbHu/Ec/aOpxXgYZlbfvhNFYiIBP9OxZn13OuxpcVIzEgnUl006wOu55oFlD\nfcm56mCYzLdOj4O1kUFOnc+ya9Tov76jhWddLFcplKt99axX42pTt77n2AKxoL/pbjYWNI5VyyCd\nsenO0myyu2ksw30oOZ9J5h0FFwF8PsF4ovdc627vODpFpZ/1MiBhweW+IIpWnvWMLQ2yVXoc1AKM\njbf1bqOM9VK2yFMLWXaNGS19p0aiLGZLdd33rKDnOvKsr9wxhBBwdC5TNyxXoQOM3bGpjHW2WCZV\nKHctg4C3Pa2dVi8qjIZJvRnrc8t517rt2RmOhYzxTT3MO5xPFxGiuWFQr4zGmzvbzdjkpPZl3SWi\nQT9Bj4e8qiG0c+kiZ5Zy7LY8a+N/e5Ax7fGYMSckwgEOTCSA1iXwNRlkU5khx2yqszTrYBqL121S\nz2eKJPNlpkZW7q29EqMtBpR2Q7ZYJpkvuzZ0wI6T8VSNLGQKjMRCnujpQF36Xr1n3b4HRz882FDA\nRyIc4PHpJJWqtGQQ1YfdLoUkrY5768ezhloKX6ueLtqz7o5NZaydTGNR3o1XxvrQTAqAi7cOOt7H\nWMJZcYfinJm255VnDbTMZ+6U+VTRtQkxdpTEdb7OWOdJhAPEQ/62QcZUwduOe3aGokEePr0EGJkg\nYGjWAKftnnWhP1WV3XLNziFgZc9aa9adsb4+WY+ZSTnwrJUM4lFPa8tYbxlwvI+xeMgcuCsdNfE5\n5+Ai1ikjsd5lpIVMwfXgIkDAb/TutmvWc2ar2oDPt6Jn3S+5YSQe5NEzRkMk5VlPJMKE/L46TV3J\nIOtJswa7Z91srNXQ3LDOBumITXVJU+ltE10E8yJBoyzWS896IBLoSbMeSxiDc52OqFKetSfZIC2C\neN1iVC+6G1xUNJaczySNNMqdo9H2AcZ8ydPBA3ZUxkoo4LM+H59PsH04UieDpArrUwa5ZNsAIb+v\npbGOas+6KzbVWZpJ5okEfV3dKgohGIp51x/k0Lk0F28Z6KmtZW2MkjODeM6DUnPFcLSW0eCU+XTB\n9WIdxUhD0c5Mygj27hiJcXox1zKLxevBA3bUnd3OkWhdHv7USHTdBxjB0KN/+cZd3Hxxc/GMDjB2\nx6Y6S2roQLeG0aue1lJKnpxJcdFW5xII1Cr7FjLOMkJmlvMMRgKuNfa3M2wbrOqEYrlKMl/2RLMG\n40KnyuGllNZ3ZMdIlPQKrUj75cEqz1pJIIodw7E6mSbZxykx3fK2n72cl9+wq2m5GvOlA4ydscmM\ntbN8Zq/apM6mCiznSj3p1QCjZmWf0yDj2WV3hw7YCQV8xEN+xxe781ZBjDee9bCtTepyrkSxXGVy\nMGKlx7XSrfvZg0Np/rvNHGvF1EiUuVSBvJkSmS6UCfrFhpIUbrlkkt949n4ru0WzMhvnk3WB2ZSz\nSkGvjPWT54zg4kU9GuuxHmWQ6eUc2z38wfTSeU/lj3sRYISaZi2ltAqLDBnEOB+NGSGVqjSNdX88\n2OF2nrV5fNOmFLKULfZtSoxbbB+O8ub/eolnPXcuNDaNsTZucZ1V6Q17ZKxVJshFWxI97WfMkkGc\nGcQzizlPvZuRuPNmTrWp5t4Y65FYiIJZtFNrnxthZxvPWvVm7p+xNjzrRmNt5Vov5fjukXk+/8PT\nXLd7pC/HpFkb1p/A5RHpQplsseIo62IwGmTZA8360EyK8USo51v8WChANOi3yrK7IVsss5gt9VSU\nsxqNE0+6Qd0tuN3ESaGqGM9nirbBFGGGYkEGIgFONWSE9KsviOLKqSF2j8W4asdQ3XL1eX39sRm+\n+MAZ9o0nePfLrunLMWnWhk3jWdducZ1p1qlCmUrVeX+LVjw5k+5ZAlE4nXqibqO99Kx7kUFU0NQr\nGcTe2c7uWQNWRogdqz1quD+a9cEtA9z1h89pmiK0dTCC3yf4+D1PMRgJcOdrbmg5BEBz4bBpjHXj\nD7Eb1K2om+l71ark8EzKNWM9nggx78BYK2PkqQzSpsl/Jyyki4TNsmsvUHng5zNFZpNGVozK/90x\n0pxrnV4nWRcBv4+p4ShD0SAfu/0GzwLEmvXDppFBnPQFUdSqGEvWj7tXzizlyBYrXNxj2p5iNB5i\nzoEMMr1kXMQ8DTBGgyTzJSpVib/LYNJ8ush4ovNhEd0yEqsV7RgB6JrR2zkS4zuH5+sqQ1PrqFLw\nL192NUPRIAcm3fkOadY3m8+zdiiDgLv9QdzKBFGMJcKOskHOLGUJ+IQnpeaK4VgIKZ2dP69KzRWj\nNs+6sfvhjpEouVKlTl5KraMeHNfvGeWgS98fzfpnExnrAvGQ39Ht9LAHnfcOzbqTCaIYi4dYMFPQ\numF6ycix7tbj7QY1WNWJbj2fLnhWEAPGhVgIo9HUTLJQl4dvNUyyl3X3abK5RtPI5jHWKefN9Ydc\nKJlu5NC5FFPDUdd+9GMJo290usvxY2cWvc2xht6qGBfSRavoxwv8PsFwNMhCpmiMV7PLIGa6nD0j\nxJJB1lkPDs2Fz6Yw1lJKnjyXcmyUvBjt9eRMmoMuedXgvIrxzFKOHR4b61rGRXfHVqkahSpbh7wz\n1mAEGY/PZyhVZJ0MMtXCs07ny/hE6y5yGo2XbApj/cCpJY7Mpvnpq7Y52t5tzbpcqXJ0Nt1zmbkd\npevOd6FblytVziXznnvWTgcQzKbyVKqyD8cX4gkzhmDPFhqMBBmKBuuqGNUE8Y1UKai5MNgUxvrT\n954kHvLzs1dvd7R9OOAnGnTe36KREwtZipWqa8FFqJWcr+RZn1zI1mnaM6kClar0tCAGajJIt561\nlaky5L2xVuetMVvIaJVqb0Xav74gGo2dC95YJ/MlvvLwND93zVRPOqOb/UEeOWNM/rhsu/PpMI2o\nKsh2VYynzme5+S/+k688fNZa1o+CGDAyJ/w+0XXJ+dll4/i89qxVFSM0F03tGI41eNb96wui0di5\n4I31lx44Q75U5RUtWjR2g5vG+sGTS8RDfk8863b9QR6bTlKV8L0j89Yy1bzea2MohDAnsnR3/tTF\nZNuwtwUf9tz5iYF6z/ry7YMcX8hYF8FUvqSNtWZNuKCNtZSST957kiumBrmyobdCtwzF3DPWD5xa\n4sodQ66my0WCfuIhf9tc66NzaQDuf2rRWnamT541GOmPTmSQRDjAoMeyw6gp0wxFg0QaRkzddNEE\nUsJ3zItcP9ujajR2Lmhj/dDpZZ44l2rZ+Lxb3PKs86UKj08nuXaX+x3SRhMhzrcZQHB01jDWR2bT\nltE8s5RjNB6yyqu9ZCRWa/LfKdNLObZ77FVDzbNuVd165dQQw7Eg3z5sGOt+Dh7QaOxc0Mb60/ee\nJBby83MOA4t2hl2aFvPY9DLlquSancM976uRsXi4rQxyZC5t3b7/6KThXXvdGtXOSCzIUpuL3aNn\nlnntx+63Gukrzi7n2eZxcNE4NmWsmy8Mfp/gWQfG+fbhOaSUpLVmrVkjLlhjXa1KvvLwND995TZX\nblsNr7X7CsFGHjhpBBev9cRYh1rKIFJKjs6m+ekrt+H3Ce4/YRrrpf4Z65U6773za0/w/9o78yg7\nqjqPf37dr/eXTnpPZyFrB0gwIDSLyjYgy+gZkRElOA6gKGdEx+XMOAfmH2WQM+rocRflqAMuIwO4\nEFRUCOAyCjGQhSyQdAj0mnR3Or2mt7z+zR913+vXne5Ov73r9e9zzjuv6tatqlv3VX3rvt+99/d7\ncu8R9rT2Tkj3WtapL1+4g3E6J1+X1lVypHeY/Uf60xrZ3DCiSUisReSTIrJHRHaLyE9EpFBEykXk\nSRE54L7LovLfJSINIvKKiFwTlX6eiLzktn1NkjCItXNgmOMjoYRt1WEqSwoYCY1FYt3Fy46mbpYu\nKorLR8mpqAhO7Sa1rWeIgZEQZy1dyIYlpWx7/RiqmjYxhJMD04Z5qbknYmIIm2rAMxcdHRhhSRq8\nyY23rKeefHNJXRUAT+07wkhozFrWRkaIW6xFZCnwMaBeVc8CcoFNwJ3AFlWtA7a4dURkvdu+AbgW\n+JaIhI2l9wEfAurc59p4yxXmcI+L2J0kUaxcEA6dFV9Q2jDbG7s557Tkt6rBm8V4dGD4pNZ/gxPB\nNVVBzltRxs6mbjr6vZdZqsdYh1lUnM/Q6NhJpo77ft/AgsIA+YEcDjh/KTD++6XjZVJTWkiwIDCt\nB8Qli4pYWx3k1y95wx5NrI1MkKgZJAAUiUgAKAZageuAB932B4F3uuXrgIdUdVhVDwENwAUiUguU\nqupz6qnMD6L2iZtku/6sdOOYY5khOJn2viFaugdTYgIBzwwyGtKIZ7gwYbFeWx2kfkU5wyfGeHLv\nEQCWpqEDDya6Ig1zsKOfJ3Yf5uY3rWBNVTBSTkjfsD2AkoIAf77rCv5u4/R9G5fUVUbMNNbBaGSC\nuMVaVVuALwKNQBvQo6q/A2pUNTzz4jBQ45aXAk1Rh2h2aUvd8uT0hDjsJlQkyyl7OKxUZwIt6x1h\ne3WKWtaRWIyTXigNHf0sLMqjMphP/UrPKrV5RysASxdNjO2XKsKeC6NHhHzn9wfJz83h/W9Zxdrq\nIA0dUWLtWtbpsqmXFubNGLj10nVVkWWzWRuZIBEzSBlea3kVsAQoEZH3RedxLeWkxcISkdtFZJuI\nbOvo6Jgxb1vvEPm5OUlzr5kMM8iOpm4COcKGJcmxo09m3DfzxDI2tPeztjqIiOe3eumiIra+1gWQ\nlqFxMC7W4U7Gtp5Bfr69hU3nL6cyWMDaqiDNxwYZHPHMJOGW9VyJgHLhqnLyc73HxcwgRiZIxAzy\nVuCQqnao6ijwM+DNwBFn2sB9t7v8LcDyqP2XubQWtzw5/SRU9X5VrVfV+qqqqqmyRGhzfpqT5XCn\nvDgfEehIwAyyvbGbM2tLT5p4kSymM9UcbO9nbdW4h7/6lWWoQmFeTkTgU03ZJDepX35yP6rwoUtX\nA56JRnV88k5bzyCVwXwKAnPDu11xfiDyr8TMIEYmSESsG4GLRKTYjd64EtgHbAZucXluAR5zy5uB\nTSJSICKr8DoStzqTSa+IXOSOc3PUPnFzuGcoqa2yQG4O5cX5cZtBQmPKrubulJlAYGLUkzDHBkY4\nOjDC2uoosV7hic7SRUVp8x4XcZM6OMJjO1p4eFszH7xkNcvKPDNMuHxhsW7pTr03wFi5zJlCkhXa\nzTBiIe4mgqo+LyKPAi8CJ4DtwP1AEHhYRG4DXgfe4/LvEZGHgb0u/0dUNTw04A7gAaAIeMJ9EqKt\nd5DzkjxLsCKYT2dffGLd0N7PwEgoJZNhwoTFOtpUExa/aLE+b0U5kJ6RFmHCZpAXXj/Gb3Yf5vyV\nZfzL1esi21dWFpObI5FOxrbuQVZXlaStfLPh5jetZFVlSdrs6IYRTUL/51T108CnJyUP47Wyp8p/\nL3DvFOnbgLMSKUs0Y2PqWtbJfagqg9PPEDwVO5q8iSipFOvCPC9sWXQZo0eChDl98QIWFeexujJ9\nYliY57mZ/dmLLZSX5PP1m84lL3f8j11BIJcV5cU0tPdHxoBfXFeZtvLNhqL8XK7esDjTxTDmKVlp\nfDs6MMJoSKlNcudUZbCAnc3dce372tHj5OUKKytSK5AVwYmzGBva+ynMy5nQGszNEX764TenNLbh\nVJQV5zHUG+IrN54zpYlqTXWQA+399A6dYGAklHI/1obhJ7JSrMMTKlIh1vGaQVq7B1m8sHDG4WHJ\noLxk4izGho5+VlcGTzrvmqrkhRSbLe+uX055Sf6EYXDRrK0O8szL7RH/0XPNZm0YmSQrxbrVjbFO\nthOgimA+AyMhBkdCMXuqa+tOj1OiipICmqMCvDa096fEw188fPKqdTNuX1sV5MSY8tyrR4H0TIgx\nDL+QlY6cIlPNk9yyrgrGPzGmtWcwLX4uVlQUs/9IH195aj99Q6O0dA9OGLY3lwkHEP79fm8MvXXk\nGcY4WduyTuaEmDDhiTGd/cMsL5/9zL/QmHIkDYFpAT7+1jq6Bkb4ylMH+MX2FlQndi7OZcKmmecP\ndRHIkci4ccMwsrhlXbOwIOn24fEp57GNCOnsH/Y6PNMg1qWFeXz5xnP45nvPjfiPXlfjD7EuKQiw\nZGEhIyfGqCktTGokHcPwO1nZsk6V0/rKBTMHpZ2O8NTpdJhBwrx9Yy31K8vY3niMuiTGekw1a6qD\ntPYMmQnEMCaRlS3rtp7BpI8EgfGgtLHarNvS6O4zmprSQq49qzat50yUumrvxWKdi4YxkawT67Ex\n5UjPcEocABXm5bKgMBCzGWS8ZW2txVMRtq/bsD3DmEjWiXXX8RFGQmMpE8bKYEHMLevW7iGK83Mp\nLcpKq1NSiYj1HPG2ZxhzhawT67bu1AzbC1MZjN2ZU1uPFz4rXU6T/MzGZQu5sX45V5xZc+rMhjGP\nyLqmXltkQkyqxLqAA1ERTWZDa3dqbOjZSGFeLp+/YWOmi2EYc46sa1kf7g1PNZ9DZpCeIbNXG4aR\nEFkn1q3dQ+TlSsqcFFUE8+k+PspoaGxW+YdPhOjoG7bRDYZhJETWifXhntQ6TArPquuawlXqaGiM\nt331j3zzmYZI2pEerxVuoxsMw0iErBPrtp4haktTJ4xhse6Ywvvez19sYW9bL0/sboukhZ1KmRnE\nMIxEyEqxTmWQ1cpwBPFJLesToTG+4VrUe1t76R0adeVxHZ5mBjEMIwGySqxVvQgxqRTGSFDaSS3r\nX+xopbHrOLe+eSVj6oWvAs+GDtayNgwjMbJKrI8OeBNiaktTKNYLTnaTeiI0xjefaWB9bSmfuuZ0\nAjnCXw91Ad6wvbLivJj9XxuGYUSTVWI9PiEmda3YkvxcCvNyJoj1L3e1cahzgI9dWUdJQYA3LFvI\nVifWqXIqZRjG/CKrxDocIWV5eerEUUSoKCmIxDkMjSlff/oAZyxewNXrvVl3F6wqZ2dzN0OjIVq7\nB20kiGEYCZNVYt3YFRbr2QcGiIfKBQV0uJb19/70Kgc7BvjEW+siwwUvXFXOaEjZ3tjtxNo6Fw3D\nSIysEuumY8dZVJxHaWFeSs9TFcyns3+EhvZ+vvi7/Vy1voZrNiyObD9vRTki8Owr7fQOnTAziGEY\nCZNVYt3YNchpKW5VgzcipKNviE89upPi/Fzuvf6sCU6aFhblcebiUh7b0QpgLWvDMBImq8S6qet4\nyk0g4E057+wfYXtjN3e/YwPVC04W4wtWlUf8lJjN2jCMRPGtWI+GdMJ6aExpPnac5WXpaVkDXL2+\nhnecvWTKPBeuKo8sm8c9wzASxbdi/drRgQnrh3uHGA1pWswgF66q4JK6Sj47yfwRzflOrEW88FqG\nYRiJ4Ft/1sOjIUZDY+Tleu+bJjcSJB1ivX5JKT+87cIZ81QGC1hTVcLAcChSRsMwjHjxrVgr8GrH\nAKcv9gKsNqZRrGfLbRevpr1vKNPFMAwjC/CtWAPsbeuJiHVT13FyZG45THrvhadlugiGYWQJvv1/\nLsC+tr7IemPXcZYsKjKTg2EYWYlvla0wL5d9bb2R9cau43PKBGIYhpFMskasm9I0IcYwDCMT+Fis\nc+jsH6G9b4jjIyfo7B9Oy4QYwzCMTOBbsS7K8/xD723tpanLi8ZiYm0YRrbi29EghXm5DOJ1MoZn\nM5oZxDCMbMW3Yp2bIyxZWMi+tl7yA94fBBNrwzCyFd+KNcCZtaXsa+ulvCSfYEGAsuLUukY1DMPI\nFL61WYM37fvVzgEOtPexrKxoWj8dhmEYfsfXYn1mbSmhMWXroS4zgRiGkdX4XqyBtHnbMwzDyBS+\nFusV5cUU53tD+E6rMLE2DCN78bVY5+RIxJFTOoIOGIZhZApfizWMm0JsQoxhGNmMr4fuAVy1vobd\nLT0sL7c4h4ZhZC8JtaxFZJGIPCoiL4vIPhF5k4iUi8iTInLAfZdF5b9LRBpE5BURuSYq/TwReclt\n+5rEMAbvb06vZvNHL6YgkJvIpRiGYcxpEjWDfBX4jaqeAZwN7APuBLaoah2wxa0jIuuBTcAG4Frg\nWyISVtj7gA8Bde5zbYLlMgzDyCriFmsRWQhcCnwPQFVHVLUbuA540GV7EHinW74OeEhVh1X1ENAA\nXCAitUCpqj6nqgr8IGofwzAMg8Ra1quADuC/RWS7iHxXREqAGlVtc3kOAzVueSnQFLV/s0tb6pYn\npxuGYRiORMQ6AJwL3KeqbwQGcCaPMK6lrAmcYwIicruIbBORbR0dHck6rGEYxpwnEbFuBppV9Xm3\n/iieeB9xpg3cd7vb3gIsj9p/mUtrccuT009CVe9X1XpVra+qqkqg6IZhGP4ibrFW1cNAk4ic7pKu\nBPYCm4FbXNotwGNueTOwSUQKRGQVXkfiVmcy6RWRi9wokJuj9jEMwzBIfJz1PwM/FpF84FXg/Xgv\ngIdF5DbgdeA9AKq6R0QexhP0E8BHVDXkjnMH8ABQBDzhPoZhGIZDPLOy/6ivr9dt27ZluhiGYRgJ\nISIvqGr9qfL5frq5YRjGfMDE2jAMwweYWBuGYfgAE2vDMAwf4NsORhHpA17JdDkyTCXQmelCZJD5\nfv1gdQD+r4MVqnrKiSN+dpH6ymx6ULMZEdk2n+tgvl8/WB3A/KkDM4MYhmH4ABNrwzAMH+BnC46T\n9wAABjdJREFUsb4/0wWYA8z3Opjv1w9WBzBP6sC3HYyGYRjzCT+3rA3DMOYNaRNrEfm+iLSLyO6o\ntLNF5C8u/uLjIlIatW2j27bHbS906b8RkZ0u/dtRocEmn2/KuI4icqmIvCgiJ0TkhlRf96QyJaUO\norZvjj7WFOebrg5WiMgWEdklIs+KyLLpjpFMkngP5IvI/SKy38X/fNc055vu+m8VkQ4R2eE+H0z1\ntUeVKVl1cJNb3+Weicppzpe1z4GI3Oiuf4+IfH6G882p5yBuVDUtH7wQYOcCu6PS/gpc5pY/ANzj\nlgPALuBst14B5LrlUvctwE+BTdOcbytwkcv3BPC3Ln0lsBEvfNgN6br+ZNaBW/974H+ijxVDHTwC\n3OKWrwB+6KfrB+4GPuuWc4DKGK//VuAb6fztk1kHLr09fN3AF4DPzKfnwH03AlUu/UHgSj88B3HX\nW5p/pJWTfqAexu3my4G9bvltwI9Ocaw84HHgxim21QIvR63fBHxnUp4H0n2TJqsOgCDwJ2A904j1\nTHUA7AGWu2UBen12/U1AySnOM9P130qGxDoZdeDu/Q5ghfv9vg3cHksdRKX58jkAzscLzB1e/0fg\nWzHeBxl7DuL5ZNpmvQcvkC7AuxmPJLMOUBH5rfur9m/RO4nIb/FaFn14EWom46e4jvHUwT3Al4Dj\nMxx3pjrYidcyB7geWCAiFfFfQkLEdP0isshtv8elPyIiNZzMqe6Bd7m/xo+KyHIyS0x1oKqjwIeB\nl4BWvJf296Y4bjY/Bw3A6SKyUkQCeEG2p/od/fIcnJJMi/UHgDtE5AVgATDi0gPAxcA/uO/rReTK\n8E6qeg3eG7MA7++Ln4mpDkTkHGCNqv48gXP+K3CZiGwHLsMLoxaaeZeUEes9EMAL/fZnVT0X+Avw\nxRjP+TiwUlXfADyJ9xc6k8R6D+ThifUbgSV4ZoK70l7q5BJTHajqMbw6+F/gj8BrxH4Pz6Xn4JRk\ndLq5qr4MXA0gIuuAt7tNzcAfVLXTbfs1no1rS9S+QyLyGHCdiDwNvOA2bQbuY5ZxHTNNHHXQD9SL\nyGt4v1+1iDyLF1ZtVnWgqq24FoWIBIF3qWp3aq5wZuK4/qfx/lH8zOV7BLhNvI7m2V7/0aj07+LZ\nfDNGHHXQ6/Y76NIfBu6MpQ7mGvFogao+jvfiRURuB0Ix3gdz5jmYFRm2U1W77xy8jo4PuPUy4EWg\nGE+QnsL78YJArcsTwHurfnSac03uVHjbpO0PMDdsdTHVwUzHmm0d4Dm+yXHL9wL/4afrBx4CrnDL\ntwKPxHj9tVF5rgee89M9gNeabmO8c+0e4Evz7TmI2qcM2AGs88tzEFedpfHH+Ym7wUbx3pa3AR8H\n9rvP53AdDC7/+/DsWLuBL7i0Grxe410u/etAYJrz1bs8B4FvMN55cb47/wBwFNjjpzqY6YaPoQ5u\nAA64c34XKPDT9eN1rP3B3QdbgNNivP7/dMfdCTwDnOG3ewD4J2Cfq4PHgYr59hy44+x1nylHhc3F\n5yDej81gNAzD8AGZ7mA0DMMwZoGJtWEYhg8wsTYMw/ABJtaGYRg+wMTaMAzDB5hYG1mPiKiI/Chq\nPeC87v0yzuMtEpE7otYvj/dYhjFbTKyN+cAAcJaIFLn1q0hsJt8i4I5T5jKMJGJibcwXfs34FOab\n8CZUACAi5SLyC+fX+DkR2ejSP+N8Lz8rIq+KyMfcLp8D1ojnC/u/XFrQOYV6WUR+HPaZbBjJwsTa\nmC88BGxyjus3As9Hbbsb2K6qG4F/x5vuHOYM4BrgAuDTzonSncBBVT1HVT/l8r0R+ASeB7zVwFtS\neTHG/MPE2pgXqOouvOn5N+G1sqO5GPihy/c0UBEVqeRXqjqsniOhdjyXB1OxVVWbVXUMz0/FyuRe\ngTHfyajXPcNIM5vx3KlejhdpZDYMRy2HmP6ZmW0+w4gLa1kb84nvA3er6kuT0v+I5y8ZEbkc6FTV\n3hmO04fnc9kw0oa9/Y15g6o2A1+bYtNngO+LyC48X9m3nOI4R0Xk/1zA1yeAXyW7rIYxGfO6ZxiG\n4QPMDGIYhuEDTKwNwzB8gIm1YRiGDzCxNgzD8AEm1oZhGD7AxNowDMMHmFgbhmH4ABNrwzAMH/D/\nEHhxp7KWXm0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bd0576f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the time series of air miles flown\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "air_miles['Air miles flown'].plot(ax=ax)\n",
    "ax.set_title('Monthly air miles flown during 1963 - 1970')\n",
    "plt.savefig('plots/ch2/B07887_02_13.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adf_result = stattools.adfuller(air_miles['Air miles flown'], autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-val of the ADF test in air miles flown: 0.994502281123\n"
     ]
    }
   ],
   "source": [
    "print('p-val of the ADF test in air miles flown:', adf_result[1])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
